Method and systems for simulating a vitality metric

ABSTRACT

A system for simulating a vitality metric, the system comprising a computing device, wherein the computing device is configured to retrieve, from a user, a biotic extraction, generate a vitality metric, using a machine-learning model, wherein generating a vitality metric further comprises training a machine-learning model with training data corresponding to measuring biotic parameters present in the biotic extraction data and determining a metric that is a summation of all individual biotic parameters present in the biotic extraction data. Computing device determines a simulated metric, using a simulation machine-learning process, wherein the simulation perturbs a biotic parameter present in the vitality metric, wherein a biotic parameter is an element of numerical data relating to an element of data present in the at least a user biotic extraction. Computing device provides, to a user, a vitality metric and at least a user effort that resulted in a simulated metric.

CROSS-REFERENCE TO RELATED APPLICATION DATA

This application is a continuation of U.S. patent application Ser. No.17/007,269, filed on Aug. 31, 2020, entitled “METHOD AND SYSTEMS FORSIMULATING A VITALITY METRIC,” which is incorporated by reference hereinin its entirety.

FIELD OF THE INVENTION

The present invention generally relates to the field ofmachine-learning. In particular, the present invention is directed to amethod and system for simulating a vitality metric.

BACKGROUND

Accurate and efficient methods of calculation of user-friendlyphysiological metrics using machine-learning is largely unknown due tothe nature of the large, variable datasets provided by users.Furthermore, providing clear and concise metrics and accurateinstructions to improve these metrics is difficult.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for simulating a vitality metric, the systemincluding a computing device, wherein the computing device is designedand configured to retrieve a biotic extraction pertaining to a user,generate a first vitality metric using a metric machine-learning modeland the biotic extraction, wherein generating the first vitality metricincludes training a metric machine-learning model with training data,the training data containing a plurality of data entries correlatingbiotic extraction data to measured biotic parameters, generating thefirst vitality metric, the first vitality metric containing a summationof all individual biotic parameters associated with the bioticextraction data, as a function of the metric machine-learning model,determine a simulated metric as a function of the generated firstvitality metric of a user, wherein determining the simulated metricincludes perturbing a biotic parameter present in the first vitalitymetric, and provide, to a user, the first vitality metric and at least auser effort that produces the simulated metric.

In another aspect, a method for simulating a vitality metric, the methodincluding retrieving, by a computing device, a biotic extractionpertaining to a user, generating, by the computing device, a firstvitality metric using a metric machine-learning model and the bioticextraction, wherein generating the first vitality metric includestraining a metric machine-learning model with training data, thetraining data containing a plurality of data entries correlating bioticextraction data to measured biotic parameters, generating the firstvitality metric, the first vitality metric containing a summation of allindividual biotic parameters associated with the biotic extraction data,as a function of the metric machine-learning model, determining, by thecomputing device, a simulated metric as a function of the generatedfirst vitality metric of a user, wherein determining the simulatedmetric includes perturbing a biotic parameter present in the firstvitality metric, and providing, by the computing device, to a user, thefirst vitality metric and at least a user effort that produces thesimulated metric.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an exemplary embodiment of a system ofsimulating a vitality metric;

FIG. 2 is a block diagram of an exemplary embodiment of amachine-learning module;

FIG. 3 is a block diagram of an exemplary embodiment of a vitalitydatabase;

FIG. 4 is a diagrammatic representation of an output of simulatedvitality metrics;

FIG. 5 is a diagrammatic representation of a user device displaying apath for a user effort;

FIG. 6 is a flow diagram illustrating an exemplary method for simulatinga vitality metric; and

FIG. 7 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for simulating vitality metrics. In an embodiment,the system may include a computing device configured to receive bioticextraction data of a user, including information regarding userinteraction with an ecological environment. In an embodiment, computingdevice may use machine-learning models to map the user biotic extractiondata to a vitality metric. In an embodiment, computing device maysimulate a plurality of vitality metrics as a function of user actionsusing a simulation machine-learning process and determine what actions auser may perform to improve the vitality score. A computing device mayprovide to a user, at least a user effort that results, for instance andwithout limitation, in an increase of vitality metric. In an embodiment,a user may select efforts that result in an increased vitality metric,as determined by the simulation algorithm, and the computing device mayguide a user to a destination for performing the effort. Computingdevice may update a vitality metric as a function of the user effort.

Referring now to FIG. 1, an exemplary embodiment of a system 100 forsimulating a vitality metric is illustrated. System includes a computingdevice 104. Computing device 104 may include any computing device asdescribed in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Computing device104 may include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. Computing device 104 mayinclude a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. Computing device 104 may interface or communicate with one ormore additional devices as described below in further detail via anetwork interface device. Network interface device may be utilized forconnecting computing device 104 to one or more of a variety of networks,and one or more devices. Examples of a network interface device include,but are not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A networkmay employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, softwareetc.) may be communicated to and/or from a computer and/or a computingdevice. computing device 104 may include but is not limited to, forexample, a computing device or cluster of computing devices in a firstlocation and a second computing device or cluster of computing devicesin a second location. Computing device 104 may include one or morecomputing devices dedicated to data storage, security, distribution oftraffic for load balancing, and the like. Computing device 104 maydistribute one or more computing tasks as described below across aplurality of computing devices of computing device, which may operate inparallel, in series, redundantly, or in any other manner used fordistribution of tasks or memory between computing devices. computingdevice 104 may be implemented using a “shared nothing” architecture inwhich data is cached at the worker, in an embodiment, this may enablescalability of system 100 and/or computing device.

With continued reference to FIG. 1, computing device 104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, computing device104 may be configured to perform a single step or sequence repeatedlyuntil a desired or commanded outcome is achieved; repetition of a stepor a sequence of steps may be performed iteratively and/or recursivelyusing outputs of previous repetitions as inputs to subsequentrepetitions, aggregating inputs and/or outputs of repetitions to producean aggregate result, reduction or decrement of one or more variablessuch as global variables, and/or division of a larger processing taskinto a set of iteratively addressed smaller processing tasks. Computingdevice 104 may perform any step or sequence of steps as described inthis disclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing.

Continuing in reference to FIG. 1, computing device 104 may retrieve,from a user, a biotic extraction 108. As used in this disclosure,“retrieve” from a user means accepting, collecting, or otherwisereceiving input from a user and/or device. A “biotic extraction,” asused in this disclosure, is data that relates to a user's health andphysiology, including chemical, biological, physical, and behavioraldata relating to a user, and how a user relates to their natural,social, and built environments. In non-limiting illustrative examples, abiotic extraction may be a combination of a user's health data includingmedical history, user diet, exercise, sleep, data corresponding totimestamps and geographical locations for how a user spends his or hertime, data regarding how a user spends money, user social mediainformation, and the like.

With continued reference to FIG. 1, biotic extraction may includewearable device data that tracks how a user relates with his or herenvironments. Wearable device data may include data and associatedanalysis corresponding to, for instance and without limitation,accelerometer data, pedometer data, gyroscope data, electrocardiography(ECG) data, electrooculography (EOG) data, bioimpedance data, bloodpressure and heart rate monitoring, oxygenation data, biosensors,fitness trackers, force monitors, motion sensors, video and voicecapture data, social media platform data, and the like. Bioticextraction data may be provided by a user, a second individual on behalfof a user, for instance and without limitation a physician, medicalprofessional, nurse, hospice care worker, mental health professional,and the like. Biotic extraction data may originate from a userquestionnaire, graphical user interface (GUI), or any other suitableforum for gathering information regarding biotic extraction. Personsskilled in the art, upon review of this disclosure in its entirety, willbe aware of the various ways in which biotic data may be collected andprovided to the system described herein.

Continuing in reference to FIG. 1, computing device 104 may generate avitality metric using a metric machine-learning model and the bioticextraction 108, wherein generating a vitality metric may includetraining a metric machine-learning model with training datacorresponding to measuring biotic parameters present in the bioticextraction 108 data, and determining a metric that is a summation of allindividual biotic parameters present in the biotic extraction 108 data.A “biotic parameter,” as used in this disclosure, is a variable elementof data relating to an element present in the at least a user bioticextraction. Biotic parameters may be qualitative elements such as binaryelements, for instance and without limitation a Boolean, ‘yes’ or ‘no’,‘true’ or ‘false’, a category name, identifier, or that like, that mayapply to an element of biotic extraction 108. In non-limitingillustrative examples, a biotic parameter may include qualitativeelements such as the presence or absence of exercise, names of the typesof activities as part of exercise, and the like. Biotic parameters maybe quantitative elements represented, for instance and withoutlimitation, as numerical values, polar coordinates, functions, matrices,and the like. In non-limiting illustrative examples, a biotic parametermay include quantitative elements used to describe frequency, duration,and intensity of exercise, number of repetitions, and the like.

Still referring to FIG. 1, a “vitality metric,” as used in thisdisclosure is a singular numerical value that summarizes a plurality ofbiotic parameters, wherein the numerical value relates to a user'soverall health, energy, and well-being, as can be determined from bioticextraction 108 data. In non-limiting illustrative examples, a vitalitymetric 112 may be a numerical value that summarizes a user's health,energy, and well-being as it relates to current nutrition, sleepdeprivation, exercise frequency, body mass index (BMI), time spentworking, time spend pursuing a leisure activity, number of friends,acquaintances, and family members, aptitude battery, financial security,mental health, and the like. A metric machine-learning model 116 maylocate discrete biotic parameters present in the biotic extraction 108data which can be assigned numerical values based on training data 120,for instance and without limitation, a numerical value which is a scoreof how ‘fit’ a user may be based on a variety of fitness categories. Acomputing device may determine a vitality metric 112 by using amathematical operation, such as addition, to obtain a final metric whichcorresponds to all categories which can be assigned numerical valuesusing the machine-learning model. There may be ranges of numericalvalues that can be applied to each parameter, for instance and withoutlimitation, as can be simulated from an input parameter, ranges ofvalues determined from training, and the like, as described in furtherdetail below.

Referring now to FIG. 2 an exemplary embodiment of a machine-learningmodule 200 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayinclude any suitable Machine-learning module may perform determinations,classification, and/or analysis steps, methods, processes, or the likeas described in this disclosure using machine learning processes. A“machine learning process,” as used in this disclosure, is a processthat automatedly uses training data 120 to generate an algorithm thatwill be performed by a computing device/module to produce outputs 204given data provided as inputs 208; this is in contrast to a non-machinelearning software program where the commands to be executed aredetermined in advance by a user and written in a programming language.

Still referring to FIG. 2, “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 120 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Training data 120 maycorrespond to at least an element of data entry that may be used fortraining, a subset of a training data 120, and/or multiple training datasets 204. Multiple data entries in training data 120 may evince one ormore trends in correlations between categories of data elements; forinstance, and without limitation, a higher value of a first data elementbelonging to a first category of data element may tend to correlate to ahigher value of a second data element belonging to a second category ofdata element, indicating a possible proportional or other mathematicalrelationship linking values belonging to the two categories. Multiplecategories of data elements may be related in training data 120according to various correlations; correlations may indicate causativeand/or predictive links between categories of data elements, which maybe modeled as relationships such as mathematical relationships bymachine-learning processes as described in further detail below.Training data 120 may be formatted and/or organized by categories ofdata elements, for instance by associating data elements with one ormore descriptors corresponding to categories of data elements. As anon-limiting example, training data 120 may include data entered instandardized forms by persons or processes, such that entry of a givendata element in a given field in a form may be mapped to one or moredescriptors of categories. Elements in training data 120 may be linkedto descriptors of categories by tags, tokens, or other data elements;for instance, and without limitation, training data 120 may be providedin fixed-length formats, formats linking positions of data to categoriessuch as comma-separated value (CSV) formats and/or self-describingformats such as extensible markup language (XML), JavaScript ObjectNotation (JSON), or the like, enabling processes or devices to detectcategories of data.

Alternatively or additionally, and continuing to refer to FIG. 2,training data 120 may include one or more elements that are notcategorized; that is, training data 120 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 120 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 120 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 120 used by machine-learning module 200 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample biotic extraction 108 input data and vitality metrics 112outputs determined from training data that relates biotic extraction 108data to ranges of numerical values that may be used as vitality metrics112.

Further referring to FIG. 2, training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 212. Training data classifier 212 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 200 may generate aclassifier using a classification algorithm, defined as a processwhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 120. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 212 may classify elements of training data to sectionsof biotic extraction 108 data as it relates to subsets of users and thecorresponding numerical values that result in the vitality metric 112.

Still referring to FIG. 2, machine-learning module 200 may be configuredto perform a lazy-learning process 216 and/or protocol, which mayalternatively be referred to as a “lazy loading” or “call-when-needed”process and/or protocol, may be a process whereby machine learning isconducted upon receipt of an input to be converted to an output, bycombining the input and training set to derive the algorithm to be usedto produce the output on demand. For instance, an initial set ofsimulations may be performed to cover an initial heuristic and/or “firstguess” at an output and/or relationship. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data 120. Heuristic may include selecting somenumber of highest-ranking associations and/or training data 120elements. Lazy learning may implement any suitable lazy learningalgorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate outputs asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail below.

Alternatively or additionally, and with continued reference to FIG. 2,machine-learning processes as described in this disclosure may be usedto generate machine-learning model 220. A “machine-learning model 220,”as used in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 220 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 220 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 120set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 2, machine-learning algorithms may include atleast a supervised machine-learning process 224. At least a supervisedmachine-learning process 224, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude biotic extraction 108 data, as described above, as inputs,vitality metrics 112 as outputs, and a scoring function representing adesired form of relationship to be detected between inputs and outputs;scoring function may, for instance, seek to maximize the probabilitythat a given input and/or combination of elements inputs is associatedwith a given output to minimize the probability that a given input isnot associated with a given output. Scoring function may be expressed asa risk function representing an “expected loss” of an algorithm relatinginputs to outputs, where loss is computed as an error functionrepresenting a degree to which a prediction generated by the relation isincorrect when compared to a given input-output pair provided intraining data 120. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various possiblevariations of at least a supervised machine-learning process 224 thatmay be used to determine relation between inputs and outputs. Supervisedmachine-learning processes may include classification algorithms asdefined above.

Further referring to FIG. 2, machine learning processes may include atleast an unsupervised machine-learning processes 228. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 2, machine-learning module 200 may be designedand configured to create a machine-learning model 220 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic, or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 2, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Still referring to FIG. 2, models may be generated using alternative oradditional artificial intelligence methods, including without limitationby creating an artificial neural network, such as a convolutional neuralnetwork comprising an input layer of nodes, one or more intermediatelayers, and an output layer of nodes. Connections between nodes may becreated via the process of “training” the network, in which elementsfrom a training data 120 set are applied to the input nodes, a suitabletraining algorithm (such as Levenberg-Marquardt, conjugate gradient,simulated annealing, or other algorithms) is then used to adjust theconnections and weights between nodes in adjacent layers of the neuralnetwork to produce the desired values at the output nodes. This processis sometimes referred to as deep learning. This network may be trainedusing training data 120.

Referring back now to FIG. 1, computing device 104 generating a vitalitymetric 112 may include calculating a numerical metric for bioticparameters in the biotic extraction by using the metric machine-learningmodel 116. In non-limiting illustrative examples, metricmachine-learning model 116 may determine the numerical value using datastored and/or retrieved, for instance and without limitation, a vitalitydatabase 124, an online research repository, social media platform,expert submission, mobile lifestyle application, or the like, asdescribed in further detail below.

Referring now to FIG. 3, a non-limiting exemplary embodiment 300 of avitality database 124 is illustrated. Vitality database 124 may beimplemented, without limitation, as a relational database, a key-valueretrieval database such as a NOSQL database, or any other format orstructure for use as a database that a person skilled in the art wouldrecognize as suitable upon review of the entirety of this disclosure.Vitality database 124 may alternatively or additionally be implementedusing a distributed data storage protocol and/or data structure, such asa distributed hash table and the like. Vitality database 124 may includea plurality of data entries and/or records, as described above. Dataentries in a vitality database 124 may be flagged with or linked to oneor more additional elements of information, which may be reflected indata entry cells and/or in linked tables such as tables related by oneor more indices in a relational database. Vitality database 124 may bedesignated as an online repository of data, or other network-integrateddata repository. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various ways in which data entriesin a database may store, retrieve, organize, and/or reflect data and/orrecords as used herein, as well as categories and/or populations of dataconsistently with this disclosure.

Further referring to FIG. 3, vitality database 124 may include, withoutlimitation, a biotic extraction table 304, vitality metric table 308,user action table 312, path table 316, cohort table 320, and/orheuristic table 324. Determinations by a machine-learning process,machine-learning model, and/or scoring function may also be storedand/or retrieved from the vitality database 124, for instance innon-limiting examples a classifier describing a subset of users withalike biological extraction data as it relates to biological degradationrates. Determinations by a machine-learning model, for instance forcalculating a degradation rate and/or a machine-learning process fordetermining an antidote strategy, may also be stored and/or retrievedfrom the vitality database 124. As a non-limiting example, vitalitydatabase 124 may organize data according to one or more instructiontables. One or more vitality database 124 tables may be linked to oneanother by, for instance in a non-limiting example, common columnvalues. For instance, a common column between two tables of vitalitydatabase 124 may include an identifier of a submission, such as a formentry, textual submission, degradation rates, and the like, for instanceas defined below; as a result, a query may be able to retrieve all rowsfrom any table pertaining to a given submission or set thereof. Othercolumns may include any other category usable for organization orsubdivision of expert data, including types of expert data, names and/oridentifiers of experts submitting the data, times of submission, and thelike; persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which data from one or moretables may be linked and/or related to data in one or more other tables.

Still referring to FIG. 3, in a non-limiting embodiment, one or moretables of a vitality database 124 may include, as a non-limitingexample, a biotic extraction table 304, which may include elements ofuser biotic extraction 108 data, as described above, and any associateddata relating to wearable device data, determinations made by an expert,medical professional, physical trainer, or the like, including medicalhistory data, physiological measurements, mental health, medicalconditions, diagnoses, diseases, or any other factors for use indetermining vitality metrics 112, simulated parameters, user efforts,and/or other elements of data computing device 104 and/or system 100 maystore, retrieve, and use to determine usefulness and/or relevance ofbiotic extraction 108 data in determining vitality metrics 112,simulated parameters, and/or user efforts as described in thisdisclosure. One or more tables may include vitality metric table 308,which may include numerical values, functions, vectors, matrices,coordinates, graphical data, parameters, and the like, for instance andwithout limitation, that link user biotic extraction 108 to ranges ofthe above. Vitality metric table 308 may include simulated parametersrelated to calculating a vitality metric and associated bioticextraction 108 data. One or more tables may include a user effort table312, which may correlate user efforts, actions, or other tasks that auser may perform to influence a vitality metric as it pertains to adetermination about vitality metric 112, simulated parameter, usereffort, path, and the like, including any outcomes, models, heuristics,scores and/or combinations thereof as they may correspond to rankings,determination, calculations, or combinations of items listed asnumerical values, metrics, functions, vectors, matrices, and the like,that corresponds to determining a vitality metric 112. One or moretables may include, without limitation, a path table 316 which maycontain one or more inputs identifying one or more categories of data,for instance locations to fitness centers, gyms, mental healthprofessionals, clinics, hospitals, health food stores, libraries, andthe like. Path table 316 may contain and organize elements ofspaciotemporal data such as geographical destinations, locations, globalpositioning system (GPS) coordinates, and the like, of where a user islocated and/or nearby locations that may be prescribed to a user forperforming a user effort. One or more tables may include, withoutlimitation, a cohort category table 320 which may contain one or moreinputs identifying one or more categories of data, for instancedemographic data, lifestyle data, physiological data, sleep patterndata, or the like, with regard to which users having matching or similardata may be expected to have similar vitality metrics 112, social mediacontacts, and/or user actions as a result of a machine-learning processdetermination, simulation algorithm, ranking algorithm output elementsand/or other data input elements. One or more tables may include,without limitation, a heuristic table 324, which may include one or moreinputs describing potential mathematical relationships between at leastan element of user data and, for instance and without limitation, bioticextraction 108 data, vitality metrics 112, simulated parameters, and/oruser actions as a result of a machine-learning process determination,simulation outputs, and rankings thereof, and how they may change as afunction of a user effort, as described in further detail below.

Referring back now to FIG. 1, vitality metric 112 may be determined as afunction of at least a user effort 128, wherein a vitality metric 112 isprovided periodically to a user as a function of the user efforts 128. A“user effort,” as used in this disclosure, is a user act, measure,activity, movement, work, or the like, that a user participates in orperforms to influence a vitality metric 112. A vitality metric 112 maybe periodically updated as a function of a user effort 128. Periodicallymay refer to any sampling of time. In non-limiting illustrativeexamples, a vitality metric 112 may be updated and recalculated using ametric machine-learning model 116 iteratively as soon as an identifieduser action 128 is performed. In further non-limiting illustrativeexamples, a vitality metric 112 may be stored and/or retrieved alongsidea series of simulated parameters which are theoretical and/or expectedvitality metrics 112 according to user efforts 128, wherein as soon as auser effort 128 is performed, the magnitude, amount, time, or degree towhich the user action 128 was performed can be used to adjust, modify,or otherwise recalculate the vitality metric 112 in predeterminedincrements. In such an example, this may be done to periodically providean accurate vitality metric 112, wherein periodic may refer toinstantaneous updates to the vitality metric 112.

Continuing in reference to FIG. 1, computing device 104 may determine asimulated metric 132, using a simulation machine-learning process 136and a generated vitality metric 112 of a user, wherein determining asimulated metric 132 may include generating a simulationmachine-learning process 136, wherein the simulation machine-learningprocess 136 perturbs a biotic parameter present in the vitality metric112, wherein a biotic parameter is a variable element of data relatingto an element present in the at least a user biotic extraction 108. A“biotic parameter,” as used in this disclosure, is an element of datadescribed as a quantitative variable, such as a numerical value, and/oran element of data described by a qualitative variable such as a Booleandata type (true/false, yes/no), a category, and the like, relating to abiotic extraction 108 datum. As used in this disclosure, “perturbing” aparameter is selecting at least a value of a parameter, where the atleast a value of the parameter is sampled from and/or through a range ofvalues, instances, or the like, either in a random and/or guided manner;for instance, perturbing may include selecting and/or inputting aplurality of values for the parameter. Perturbing a parameter mayinclude selecting a ‘stand-in’ value for that parameter, wherein thestand-in value may take the place of an original value. The perturbedvalue may be a quantitative value, such as a numerical value, selectedrandomly, for instance as in selecting any value within a finite rangewherein the value is selected with equal probability of selecting any ofthe values. Alternatively or additionally, perturbing parameters mayinclude selecting values in a guided manner, for instance such as instarting with a value and moving in whole number increments in anincreasing manner. The perturbed value may be a qualitative value suchas a discrete category such as ‘exercise’, a type such as ‘swimming’, orthe like, that may be selected among a range of discrete categories.

For instance in non-limiting illustrative examples, a biotic parameterperturbed by a simulation machine-learning process 136 in generating anoutput may be quantitative parameters of lengths of time of exercisecombined with qualitative categories of exercise such as biking,swimming, weightlifting, etc. In such an example, a simulationmachine-learning process 136 may generate a simulated metric 132 forvarying lengths of time of exercise, in varying increments of time, foreach category of exercise. A “simulated metric,” as used in thisdisclosure, is an output describing a vitality metric as a direct resultof perturbing a parameter that can be affected by a user effort 128. Innon-limiting illustrative examples, a simulated metric 132 may be anoutput describing a vitality metric 112 after applying various userefforts 128, such as choosing to exercise, alter sleep patterns, alterdiet patterns, seek and maintain counseling, and the like.

With continued reference to FIG. 1, a simulation machine-learningprocess 136 used to generate a simulated metric 132 may be anycomputational algorithm, method, or the like, that may generate anoutput, of a plurality of outputs, given a range of input values thatthe simulation algorithm may select—in a random and/or guided manner—toprovide a plurality of observations, outputs, or the like, wherein thenature of the outputs is not entirely known. Simulation machine-learningprocess 136 may be a stochastic simulation process such as Markov ModelMonte Carlo (MMMC) simulations, McKean-Vlasov processes, Monte Carlolocalization, stochastic tunneling, among other probabilistic stochasticheuristics that randomly select numerical parameters from within adefined set of parameters and calculate an outcome for all selectedparameters. Simulation machine-learning process 136 may be aprobabilistic technique for approximating global optimum of a givenfunction, matrix, vector space, or the like, such as simulated annealingalgorithm, interacting Metropolis-Hasting algorithms, quantum annealing,Tabu search, Dual-phase evolution, reactive search optimization, and thelike.

With continued reference to FIG. 1, simulation machine-learning process136 may perform a computational simulation by randomly perturbingparameters, such as user efforts, biotic parameters, and the like, anddetermine the effect on a vitality metric 112, to determine whichparameters result in an outcome that may be the same or different than afirst input vitality metric 112. Simulation machine-learning process 136may calculate the output for a simulated metric 132 for all valueswithin a range of values corresponding to a parameter in the bioticextraction 108 data; alternatively or additionally simulation algorithmmay generate a simulated metric 132 for values of parameters outside ofthe biotic extraction 108 data that was originally retrieved bycomputing device 108. For instance in non-limiting illustrativeexamples, a simulation machine-learning process 136 may calculate asimulated metric 132 for all values of exercise, wherein user effortsare time amounts in 1-minute increments over weekly periods. In such anexample, a user vitality metric 112 may increase with daily exercise forcertain efforts such as running, biking, hiking, swimming,weightlifting, and the like, wherein values above 3-hour daily exerciseresult in sharp decreases in vitality metric 112 over severalconsecutive days, wherein values at 0.5-2 hour daily exercise increasevitality metric 112 over several consecutive weeks. Such a simulationmay find that, with all other biotic extraction 108 data held constant,that maximal increase in vitality metric 112 was given specifically forrunning, biking, or swimming at 1-1.25 hour daily exercise over at leastthe next 7 consecutive days, given between 1-2 days of not exercisingfor every 5 days. A simulation machine-learning process 136 performed bya computing device 104 may retrieve, for instance from a vitalitydatabase 124, the metric machine-learning model 116, or any similarmachine-learning model trained as described above, to accurately andquickly determine how each parameters selected of the range ofparameters may influence a vitality metric 112. In such an example,simulation machine-learning process 136 may then generate a largedataset of simulated metrics 132 based on what a user may hypotheticallydo; these data may be stored and/or retrieved from a vitality database124 to more quickly update a user vitality metric 112 in the event theyperform user efforts that resemble what was observed in the simulation.

With continued reference to FIG. 1, in non-limiting illustrativeexamples, a simulation machine-learning process 136 may be a Monte Carloalgorithm, or similar simulation algorithm as described above. A MonteCarlo simulation is a mathematical technique that may generatevariables, numerical values, and the like, for modeling risk, outcomes,uncertainty, etc., of a certain system using a stochastic simulationprocess. Monte Carlo simulations may encompass a range of algorithms andmathematical analysis techniques such as A Monte Carlo simulation maygenerate a series of numerical values represented by traces, curves,functions, and the like, wherein each function may represent asufficiently good solution and/or outcome to an optimization problem,wherein the solution may be represented by a polar coordinate, vector,function, or the like, that represents, for instance and withoutlimitation, how a vitality metric 112 improves from implementing a usereffort, wherein the order, magnitude, timing, and the like, of the usereffort may be perturbed for each simulation. Each generated simulatedmetric 132 may have an associated parameter, wherein each parameter froma simulation may have associated with it a user effort, time amount,biotic extraction 108 element, or the like. Persons skilled in the art,upon review of this disclosure in its entirety, will be aware of thevarious simulation algorithms that may be performed by a processor tosample parameters associated with a medical treatment and calculate anassociated prognosis value.

Continuing in reference to FIG. 1, computing device 104 determining asimulated metric 132 of a user may include generating a simulatedvitality metric 112, using a first vitality metric 112 of a user as aninput and a simulation machine-learning process 136, wherein thesimulation machine-learning process 136 generates a simulated metric 132corresponding to a numerical output, wherein the simulated metric 132 isa vitality metric 112 after applying a user effort, and providing, to auser, at least a simulated vitality metric 112 as a function of aplurality of user efforts. Simulation machine-learning process 136 mayaccept an input of a first vitality metric 112 of a user as a simulation‘seed’, wherein the initial vitality metric 112 and associated dataexists as a reference metric that a simulation may apply user efforts128 to generate an output of a simulated metric 132. Computing device104 may then provide to a user a simulated metric 112, of a plurality ofsimulated metrics 112, and at least an accompanying user effort 128, ofa plurality of user efforts 128, that had an effect on a first vitalitymetric 112 to generate a simulated metric 112. In a non-limitingexemplary embodiment, providing user efforts 128 to a user and theassociated effects of the efforts in the form of simulated metrics 112may inform a user to pursue or otherwise select user efforts 128,wherein selecting a user effort 128 may provide a user with additionalinformation to perform the user effort 128. This may allow a user tohave a desired effect on their vitality metric 112.

Continuing in reference to FIG. 1, computing device 104 determining asimulated metric 132 may include identifying, as a function of thesimulation machine-learning process 136 output, parameters that resultin a simulated metric 132 that represent an improved vitality metric112. Computing device 104 may identify a simulated metric 132representing an improved vitality metric 112 by calculating a numericaldifference by performing a mathematical operation, for instance andwithout limitation subtraction, between a vitality metric 112 and asimulated metric 132 to determine if a simulated metric 132 representsan improved metric. Computing device 104 may then identify theparameters the simulation machine-learning process 136 selected toresult in the simulated metric 132 that represents a potential improvedmetric. In non-limiting illustrative examples, the simulation parametersmay relate to user efforts 128, such as increased vitamin and nutrientintake, wherein increased vitamin and nutrient intake may be achieved byeating more leafy green vegetables.

Continuing in reference to FIG. 1, computing device 104 simulating avitality metric 112 may include ranking, using a rankingmachine-learning process 140, user efforts 128 as a function of thenumerical change of the vitality metric 112. Computing device 104 mayidentify simulated metrics 132 and the associated user efforts 128 thatrepresent improved vitality metrics 112, as described above, and rankthe user efforts 128 using a ranking machine-learning process 140. Aranking machine-learning process may be a machine-learning algorithm,such as a supervised machine-learning algorithm, as described above,which may rank elements based on some criteria. A ranking algorithm maybe any algorithm, as described above, for classification, whereinclassification may be performed as a ranking of inputs to generateoutputs classified into a ranked list, provided a criterion for ranking.In non-limiting illustrative examples, the ranking may be a limitationlogistic regression and/or naive Bayes ranking algorithm, nearestneighbor algorithm such as k-nearest neighbors, support vector machines,least squares support vector machines, fisher's linear discriminant,quadratic classifiers, decision trees, boosted trees, random forestclassifiers, learning vector quantization, and/or neural network-basedalgorithms, as described above. In non-limiting illustrative examples,ranking criteria used by a ranking machine-learning process 140 forranking user efforts 128 may include ranking based on magnitude ofimpact on a vitality metric 112. In such an example, user efforts 128that resulted in an increased vitality metric 112 may be ranked basedupon the magnitude of their effect on increasing a vitality metric 112.Alternatively or additionally, user efforts 128 may be ranked as afunction of their addressability, difficulty of user, and the like,according to a user's environment, and the like.

Referring now to FIG. 4, a non-limiting exemplary embodiment 400 of anoutput of a plurality of simulated metrics 132 generated by a simulationmachine-learning process 136 with ranked user efforts 128 as a functionof the simulated metrics 132 and a ranking machine-learning process 140is illustrated. A vitality metric (white circle; at initial time (To))may be used as an initial value for a simulation algorithm. Simulationmachine-learning process 136 may then apply user efforts 128 to thevitality metric 112 which will result in traces (denoted by linesbetween points) that ultimately result in simulated metrics 132 atvarious time points after a vitality metric 112 (on the order of days asshown in FIG. 4). Simulation algorithm may use a threshold (denoted as ahorizontal dashed line), wherein the threshold metric value is thecurrent trajectory of the vitality metric 112. Such a threshold metricvalue may be used to determine which simulated metrics 132 have anincrease in vitality metric (denoted as grey circles) and which decreasea vitality metric (denoted as black circles). User efforts 128 (tenshown; numbered as 1-10) that resulted in simulated metrics with, forinstance and without limitation, increases in a vitality metric, maythen be stored and/or retrieved alongside the simulated metric 132 data,wherein the user efforts 128 may be ranked using a rankingmachine-learning process 140 and provided to a user to inform futureuser efforts. Persons skilled in the art may appreciate that asimulation algorithm may generate many orders of magnitude largerdatasets of simulated metrics 132 than is illustrated in FIG. 4.

Referring back to FIG. 1, computing device 104 is configured to provide,to a user, a vitality metric 112 and at least a user effort 128 thatresulted in a simulated metric 132. Computing device 104 may provide atleast a vitality metric 112 and a user effort 128 via a user device. Auser device may be a smartphone, laptop, tablet, or any other devicewith capabilities of a computing device as described herein. Computingdevice 104 may provide to a user a simulated metric 132. Computingdevice 104 may provide, display, or otherwise communicate the aboveusing a graphical user interface (GUI), or any other interface suitablefor displaying graphics, text, and the like. Persons skilled in the art,upon review of this disclosure in its entirety, will be aware of thevarious ways in which the data herein may be provided to a user,including what devices may be suitable as user devices for providing thedata.

Continuing in reference to FIG. 1, computing device 104 providing to auser, a vitality metric 112 and a user effort 128 may includedetermining spatial data corresponding to a user and calculating, usinga mapping algorithm, a path to a user effort location for the user toperform the user effort 128. As used in this disclosure, “spatial data”are coordinates that refer to a user's location on a map and anassociated timestamp that places a user at a location at a specifictime. Spatial data 144 may include information regarding a floor of abuilding, street address, or the like, and the times of being at eachlocation, wherein the times and location may indicate a mode oftransportation. Spatial data 144 may be global positioning system (GPS)coordinates, geographical coordinates such as longitude and latitudedegrees, an address, or any other data identifiable as location datathat may be used to navigate using a map. A “mapping algorithm” as usedin this disclosure is an algorithm, or series of algorithms found inmapping software, or the like, that can be used to generate drivingdirections, walking directions, or the like, using spatial data 144.Mapping machine-learning process 148 may be a machine-learning processthat can “call” or otherwise execute a commercial web-based mappingtool, application, and/or service such as, for instance and withoutlimitation, GOOGLE MAPS, that is integrated into a user device fornavigation purposes. In non-limiting illustrative examples, mappingmachine-learning process 148 may iteratively determine outputs using themapping tool as a function of updated user spatial data 144, among otherinputs. A “path” as used in this disclosure is a navigation path that isprovided via a map as directions to a location. A path 152 may includedirections, such as walking directions and/or driving directions from auser's current location to a user effort 128 location as determined bythe mapping machine-learning process 148. In non-limiting illustrativeexamples, a path 152 may include direction for a user to locate thenearest gym that has a stationary bike for a certain amount of time ofbiking exercise.

Continuing in reference to FIG. 1, ranked user efforts 128 fromsimulated metrics 132 may be provided to a user to be selected via auser interface, wherein selecting a user effort may include providing aninstruction to perform the effort. An “instruction,” as used in thisdisclosure, is an element of information provided to a user forperforming a user effort 128. In non-limiting illustrative examples, aninstruction may be to eat a meal with particular phytonutrients that arelacking, wherein the instruction is to go to a particular restaurant,cafeteria, dining hall, or the like, where menu items can be found, andfollow a path 152 to reach the particular location. Alternatively oradditionally, a user may select a user effort 128 from a ranked queue ofefforts to improve a vitality metric 112 to a degree determined in thesimulated metric 132, wherein the effort is to seek mental healthcounseling. In such an example, an instruction may be a location to amental health professional, a phone number to contact a mental healthprofessional, among other instructions a user may follow to perform theuser effort 128. Selection of a user effort 128 may be performed via aGUI, or other suitable user interface for selecting text, graphics, orthe like. Persons skilled in the art upon review of the disclosure inits entirety will be aware of the various ways in which user efforts maybe provided to a user via a user device and selected using a userinterface.

Referring now to FIG. 5, a non-limiting exemplary embodiment 500 of auser device 504 providing a path 152 to a user effort 128 isillustrated. User device 504 may provide to a user a path 152 based onspatial data 144 that refers to a user effort 128 location and a user'scurrent location. User device 504 may provide a path 152 via a mappingmachine-learning process 148 used by the computing device 104. Innon-limiting example embodiments, a user may select a user effort 128location for a path to be determined.

Referring back to FIG. 1, computing device 104 may be configured toreceive at least a user effort 128 from a user, generate a secondvitality metric 112 as a function of at least a user effort 128 usingthe metric machine-learning model 116, wherein calculating a secondvitality metric 112 may include determining how a user effort 128 hasimpacted a numerical parameter corresponding to a first vitality metric112. Computing device 104 may use the metric machine-learning model 116to generate a second vitality metric 112, wherein a second vitalitymetric 112 may be an updated vitality metric 112 that is determined withbiotic extraction 108 data that is updated to reflect a user effort 128,as described above. Calculating a second vitality metric 112 may includeusing the trained model to recalculate the vitality metric 112 based onprior determinations corresponding to the magnitude of numerical impacta user effort 128 may have.

Continuing in reference to FIG. 1, computing device 104 may identify anumerical difference between a first vitality metric and a secondvitality metric, wherein determining a numerical different includesdetermining how user efforts 128 impacted the metric. Computing device104 may use any mathematical operation, for instance and withoutlimitation subtraction, to determine a numerical difference between twoor more vitality metrics 112. Computing device 104 may compare the setsof biotic extraction data and/or user efforts between the two or morevitality metrics to identify if the numerical difference in metric maybe attributed to a user effort 128.

Referring now to FIG. 6, an exemplary embodiment of a method 600 ofsimulating a vitality metric is illustrated. At step 605, computingdevice 104 receives, from a user, a biotic extraction 108. Bioticextraction 108 may include data from a wearable device; this may beimplemented, without limitation, as described above in reference toFIGS. 1-6.

With continued reference to FIG. 6, at step 610, computing device 104generates a vitality metric 112 using a metric machine-learning model116 and the biotic extraction 108, wherein generating a vitality metric112 may include training a metric machine-learning model 116 withtraining data 120 corresponding to measuring biotic parameters presentin the biotic extraction 108 data, and determining a metric that is asummation of all individual biotic parameters present in the bioticextraction 108 data. Generating a vitality metric 112 may includecalculating a numerical metric for biotic parameters in the bioticextraction by using the metric machine-learning model 116. A vitalitymetric 112 may be determined as a function of at least a user effort128, wherein a vitality metric 112 is provided periodically to a user asa function of the user efforts 128; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-6.

With continued reference to FIG. 6, at step 615, computing device 104determines a simulated metric 132, using a simulation machine-learningprocess 136 and a generated vitality metric 112 of a user, whereindetermining a simulated metric 132 may include generating a simulationmachine-learning process 136, wherein the simulation machine-learningprocess 136 perturbs a biotic parameter present in the vitality metric112, wherein a biotic parameter is a variable element of data relatingto an element present in the at least a user biotic extraction 108.Determining a simulated metric 132 of a user may include generating asimulated vitality metric, using a first vitality metric 112 of a useras an input and a simulation machine-learning process 136, wherein thesimulation machine-learning process 136 generates a simulated metric 132corresponding to a numerical output, wherein the simulated metric 132 isa vitality metric 112 after applying a user effort 128, and providing,to a user, at least a simulated vitality metric as a function of aplurality of user efforts 128. Determining a simulated metric 132 mayinclude identifying, as a function of the simulation machine-learningprocess 136 output, parameters that result in a simulated metric 132that represent an improved vitality metric 112. Simulating a vitalitymetric 112 may include ranking, using a ranking machine-learning process140, user efforts 128 as a function of the numerical change of thevitality metric 112. Ranked user efforts 128 from simulated metrics 132are provided to a user to be selected via a user interface, whereinselecting a user effort may include providing an instruction to performthe effort; this may be implemented, without limitation, as describedabove in reference to FIGS. 1-6.

With continued reference to FIG. 6, at step 620, computing device 104provides to a user, at least a simulated vitality metric 112 as afunction of a plurality of user efforts 128. Providing, to a user, avitality metric 112 and a user effort 128 may include determiningspatial data 144 corresponding to a user, and calculating, using amapping machine-learning process 148, a path 152 to a user effort 128location for the user to perform the user effort 128. Computing device104 may receive at least a user effort 128 from a user, generate asecond vitality metric as a function of at least a user effort 128 usingthe metric machine-learning model 116, wherein calculating a secondvitality metric may include determining how a user effort 128 hasimpacted a numerical parameter corresponding to a first vitality metric112, and identify a numerical difference between a first vitality metricand a second vitality metric, wherein determining a numerical differentincludes determining how user efforts 128 impacted the metric; this maybe implemented, without limitation, as described above in reference toFIGS. 1-6.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 7 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 700 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 700 includes a processor 704 and a memory708 that communicate with each other, and with other components, via abus 712. Bus 712 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 704 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 704 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 704 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC)

Memory 708 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 716 (BIOS), including basic routines that help totransfer information between elements within computer system 700, suchas during start-up, may be stored in memory 708. Memory 708 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 720 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 708 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 700 may also include a storage device 724. Examples of astorage device (e.g., storage device 724) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 724 may be connected to bus 712 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 724 (or one or morecomponents thereof) may be removably interfaced with computer system 700(e.g., via an external port connector (not shown)). Particularly,storage device 724 and an associated machine-readable medium 728 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 700. In one example, software 720 may reside, completelyor partially, within machine-readable medium 728. In another example,software 720 may reside, completely or partially, within processor 704.

Computer system 700 may also include an input device 732. In oneexample, a user of computer system 700 may enter commands and/or otherinformation into computer system 700 via input device 732. Examples ofan input device 732 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 732may be interfaced to bus 712 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 712, and any combinations thereof. Input device 732 mayinclude a touch screen interface that may be a part of or separate fromdisplay 736, discussed further below. Input device 732 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 700 via storage device 724 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 740. A network interfacedevice, such as network interface device 740, may be utilized forconnecting computer system 700 to one or more of a variety of networks,such as network 744, and one or more remote devices 748 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 744,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 720,etc.) may be communicated to and/or from computer system 700 via networkinterface device 740.

Computer system 700 may further include a video display adapter 752 forcommunicating a displayable image to a display device, such as displaydevice 736. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 752 and display device 736 may be utilized incombination with processor 704 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 700 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 712 via a peripheral interface 756. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An apparatus for simulating a vitality metric, the apparatus comprising: at least a processor; and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to: retrieve a biotic extraction pertaining to a user; generate a first vitality metric using a metric machine-learning model and the biotic extraction, wherein generating the first vitality metric further comprises: training a metric machine-learning model with training data, the training data containing a plurality of data entries correlating the biotic extraction data to measured biotic parameters pertaining to the user; and generating the first vitality metric, the first vitality metric containing a summation of all individual biotic parameters associated with the biotic extraction data, as a function of the metric machine-learning model; determine a simulated metric as a function of the generated first vitality metric of a user, wherein determining the simulated metric further comprises: inputting the first vitality metric into a simulation machine-learning process; perturbing a biotic parameter present in the first vitality metric as a function of the simulation machine-learning process; and determining, as a function of the output of the simulation machine-learning process, the simulated metric; and provide, to a user, the first vitality metric and at least a user effort that produces the simulated metric.
 2. The apparatus of claim 1, wherein the biotic extraction further comprises data containing information of a user interaction with an ecological environment.
 3. The apparatus of claim 1, wherein perturbing the biotic parameter further comprises selecting a value of the biotic parameter, wherein the value of the biotic parameter is sampled from a range of values in a random manner.
 4. The apparatus of claim 1, wherein the simulated metric is an output describing a vitality metric as a function of perturbing a parameter that can be affected by a user effort.
 5. The apparatus of claim 1, wherein the simulation machine-learning process further comprises calculating the output for a simulated metric for all values within a range of values corresponding to a parameter in the biotic extraction data.
 6. The apparatus of claim 1, wherein the simulation machine-learning process further comprises a computational simulation by randomly perturbing parameters and determining the effect on a vitality metric to determine which parameters result in an outcome that may be the same or different than a first input vitality metric.
 7. The apparatus of claim 1, further comprising displaying to a user, a vitality metric and at least a user effort.
 8. The apparatus of claim 1, wherein determining the simulated metric further comprises utilizing the generated first vitality metric of the user and a user effort.
 9. The apparatus of claim 1, wherein the biotic parameter further comprises data relating to an element in the at least a user biotic extraction.
 10. The system of claim 1 further comprising: receiving an indication from a user that the at least a user effort has been performed; generating a second vitality metric as a function of the at least a user effort using the metric machine-learning model, wherein generating the second vitality metric further comprises determining how the at least a user effort has impacted a numerical parameter corresponding to the first vitality metric; and identifying a numerical difference between the first vitality metric and the second vitality metric, wherein determining the numerical difference includes determining how the at least a user effort impacted the second vitality metric.
 11. A method for simulating a vitality metric, the method comprising: retrieving, by a processor, a biotic extraction pertaining to a user; generating, by the processor, a first vitality metric using a metric machine-learning model and the biotic extraction, wherein generating the first vitality metric further comprises: training a metric machine-learning model with training data, the training data containing a plurality of data entries correlating the biotic extraction data to measured biotic parameters pertaining to the user; and generating the first vitality metric, the first vitality metric containing a summation of all individual biotic parameters associated with the biotic extraction data, as a function of the metric machine-learning model; determining, by the processor, a simulated metric as a function of the generated first vitality metric of a user, wherein determining the simulated metric further comprises: inputting the first vitality metric into a simulation machine-learning process; perturbing a biotic parameter present in the first vitality metric as a function of the simulation machine-learning process; and determining, as a function of the output of the simulation machine-learning process, the simulated metric; and providing, by the processor, to a user, the first vitality metric and at least a user effort that produces the simulated metric.
 12. The method of claim 11, wherein the biotic extraction further comprises data containing information of a user interaction with an ecological environment.
 13. The method of claim 11, wherein perturbing the biotic parameter further comprises selecting a value of the biotic parameter, wherein the value of the biotic parameter is sampled from a range of values in a random manner.
 14. The method of claim 11, wherein the simulated metric is an output describing a vitality metric as a function of perturbing a parameter that can be affected by a user effort.
 15. The method of claim 11, wherein the simulation machine-learning process further comprises calculating the output for a simulated metric for all values within a range of values corresponding to a parameter in the biotic extraction data.
 16. The method of claim 11, wherein the simulation machine-learning process further comprises a computational simulation by randomly perturbing parameters and determining the effect on a vitality metric to determine which parameters result in an outcome that may be the same or different than a first input vitality metric.
 17. The method of claim 11 further comprising, displaying to the user, a vitality metric and at least a user effort.
 18. The method of claim 11, wherein determining the simulated metric further comprises utilizing the generated first vitality metric of user and user effort.
 19. The method of claim 11, wherein the biotic parameter further comprises data relating to an element in the at least a user biotic extraction.
 20. The method of claim 11 further comprising: receiving an indication from a user that the at least a user effort has been performed; generating a second vitality metric as a function of the at least a user effort using the metric machine-learning model, wherein generating the second vitality metric further comprises determining how the at least a user effort has impacted a numerical parameter corresponding to the first vitality metric; and identifying a numerical difference between the first vitality metric and the second vitality metric, wherein determining the numerical difference includes determining how the at least a user effort impacted the second vitality metric. 